Method and system for modeling and processing vehicular traffic data and information and applying thereof

ABSTRACT

A method and system for modeling and processing vehicular traffic data and information, comprising: (a) transforming a spatial representation of a road network into a network of spatially interdependent and interrelated oriented road sections, for forming an oriented road section network; (b) acquiring a variety of the vehicular traffic data and information associated with the oriented road section network, from a variety of sources; (c) prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources; (d) calculating a mean normalized travel time (NTT) value for each oriented road section of said oriented road section network using the prioritized, filtered, and controlled, vehicular traffic data and information associated with each source, for forming a partial current vehicular traffic situation picture associated with each source; (e) fusing the partial current traffic situation picture associated with each source, for generating a single complete current vehicular traffic situation picture associated with entire oriented road section network; (f) predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network; and (g) using the current vehicular traffic situation picture and the future vehicular traffic situation picture for providing a variety of vehicular traffic related service applications to end users.

This application in a Divisional of U.S. patent application Ser. No.10/461,478 filed Jun. 16, 2003, which is a Continuation of U.S. patentapplication Ser. No. 09/939,620 filed Aug. 28, 2001, now U.S. Pat. No.6,587,781, which claims priority from U.S. Provisional Application No.60/227,905, filed Aug. 28, 2000.

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to the field of vehicular traffic data andinformation and, more particularly, to a method and system for modelingand processing vehicular traffic data and information, and using themodeled and processed vehicular traffic data and information forproviding a variety of vehicular traffic related service applications toend users.

Despite continuing investing in massive amounts of financial and humanresources, current road network capacities insufficiently meet the needsdictated by current levels and growth rates of traffic volume. Thisdilemma relates to current road network capacities, in general, andcurrent road network capacities in urban, suburban, and rural,environments, in particular. Road congestion, or, equivalently,inconveniently high levels or volumes of vehicular road traffic, is apersistent major factor resulting from this dilemma, and needs to begiven proper attention and taken into account for efficiently schedulingtrips, selecting travel routes, and for attempting to efficientlyallocate and exploit time, by individual drivers, as well as byvehicular traffic logistics personnel such as company vehicular fleetmanagers, responsible for performing such activities. Road congestionand associated traffic data and information also need to be wellunderstood and used by a wide variety of public and private occupationsand personnel, such as designers, planners, engineers, coordinators,traffic law makers and enforcers, directly and/or indirectly involved indesigning, planning, controlling, engineering, coordinating, andimplementing, a wide variety of activities, events, and/or constructionprojects, which depend upon accurate descriptions of current and futurevehicular traffic situations and scenarios. This situation is a maindriving force for the on-going development and application of variousmethods, systems, and devices, for acquiring, analyzing, processing, andapplying, vehicular traffic data and information.

There are various prior art techniques for acquiring, analyzing,processing, and applying, vehicular traffic data and information. A fewexamples of recent prior art in this field are U.S. Pat. No. 6,236,933,issued to Lang, entitled “Instantaneous Traffic Monitoring System”, U.S.Pat. No. 6,012,012, issued to Fleck et al., entitled “Method And SystemFor Determining Dynamic Traffic Information”, U.S. Pat. No. 6,240,364,issued to Kemer et al., entitled “Method And Device For ProvidingTraffic Information”, and, U.S. Pat. No. 5,845,227, issued to Peterson,entitled “Method And Apparatus For Providing Shortest Elapsed Time RouteAnd Tracking Information To Users”.

Prior art techniques typically include calculating velocities ofvehicles, for example, by acquiring series of exact locations of thevehicles located along roads in known time intervals, by measuringvehicular traffic flux along roads, especially, along highways, and/or,by a variety of other means known in the field. There are prior arttechniques which are either based on, or, include, the use of networksof fixed or static traffic sensors or electronic devices, such as videocameras, induction boxes, tag readers, traffic detectors, and so on,which are installed and fixed along known locations of main trafficarteries and/or traffic volume. Fixed or static traffic sensors orelectronic devices, positioned at known locations, relay crossing timesof vehicles to a computerized central traffic data and informationhandling (gathering, collecting, acquiring, analyzing, processing,communicating, distributing) system that consequently calculatesvelocities of the vehicles between two such sensors.

Significant limitations of developing and implementing comprehensive,highly accurate and precise, techniques for acquiring, analyzing,processing, and applying, vehicular traffic data and information,primarily based upon a system or network of fixed or static trafficsensors or electronic devices, are the relatively large amounts andexpense of the necessary infrastructure and maintenance, especially ifsuch resources are to account for and include vehicular traffic data andinformation associated with a plethora of minor roads characterized bylow volumes of vehicular traffic.

More recent prior art techniques are either based on, or, at leastinclude, the use of mobile sensors or electronic devices physicallylocated in or attached to vehicles, each of which is uniquely orspecifically designated or assigned to a particular vehicle, whereby themobile sensors or electronic devices automatically transmit vehiclelocations to the computerized central traffic data and informationhandling system according to pre-determined time intervals, and,whereby, vehicle velocities are relatively simple to calculate forvehicle locations acquired with sufficient accuracy.

For obtaining dynamic vehicle location and velocity data andinformation, having varying degrees of accuracy and precision, fromuniquely or specifically dedicated in-vehicle mobile sensors orelectronic devices, such prior art techniques make use of well knownglobal positioning system (GPS) and/or other types of mobile wirelesscommunication or electronic vehicular tracking technologies, such ascellular telephone or radio types of mobile wireless communicationsnetworks or systems, involving the use of corresponding mobile wirelessdevices such as cellular telephones, laptop computers, personal digitalassistants (PDAs), transceivers, and other types of telemetric devices,which are uniquely or specifically designated or assigned to aparticular vehicle. Establishing and maintaining various communicationsof the mobile sensors or electronic devices, the computerized centraltraffic data and information handling system, and, vehicular end-users,are also performed by mobile wireless communication networks or systems,such as cellular telephone mobile wireless communications networks orsystems, for example, involving the Internet.

It is noted, however, that due to the requirement of uniquely orspecifically designating or assigning each mobile sensor or electronicdevice to a particular vehicle during the process of gathering,collecting, or acquiring, the vehicular traffic data and information,the potential number of mobile sensors or electronic devices providingdynamic vehicle location and velocity data and information to thecomputerized central traffic data and information handling system islimited, in proportion to the number of vehicles featuring theparticular mobile wireless communication or electronic vehiculartracking technology. For example, currently, there is a significantlylarger potential number of vehicles associated with cellular telephonetypes of a mobile wireless communication network or system compared tothe potential number of vehicles associated with GPS types of a mobilewireless communication network or system.

Various specific techniques for manually and electronically gathering,collecting, or acquiring, vehicular traffic data and information arerelatively well developed and taught about in the prior art. Moreover,various specific techniques for electronically communicating, sending,or distributing, analyzed and processed vehicular traffic data andinformation in vehicular traffic related service applications to endusers are also relatively well developed and taught about in the priorart. However, there remains a strong on-going need for developingbetter, more comprehensive, highly accurate and precise, yet,practicable and implementable techniques for analyzing, modeling, andprocessing, the acquired, collected, or gathered, vehicular traffic dataand information. This last aspect is especially true with regard tousing vehicular traffic data and information for comprehensively, yet,accurately and practicably, describing current and predicting futurevehicular traffic situations and scenarios, from which vehicular trafficdata and information are used for providing a variety of vehiculartraffic related service applications to end users.

In the prior art, a critically important aspect requiring new andimproved understanding and enabling description for developing better,more comprehensive, highly accurate and precise, yet, practicable andimplementable techniques for analyzing, modeling, and processing, theacquired, collected, or gathered, vehicular traffic data andinformation, relates to the use of a geographical information system(GIS), or, other similarly organized and detailed spatial representationof a network of roads, for a particular local or wide area region,within which the vehicular traffic data and information are acquired,collected, or gathered. In particular, there is a need for properly andefficiently ‘spatially’ modeling a road network, and, properly andefficiently ‘spatially’ modeling, interrelating, and correlating, thevehicular traffic data and information which are acquired, collected, orgathered, among a plurality of sub-regions, sub-areas, or, otherdesignated sub-divisions, within the particular local or wide arearegion of the spatially modeled road network. Furthermore, there is aparticular need for incorporating the factor or dimension of time, forproperly and efficiently ‘spatially and temporally’ defining,interrelating, and correlating, the vehicular traffic data andinformation which are acquired, collected, or gathered, among theplurality of sub-regions, sub-areas, or, other designated sub-divisions,within the particular local or wide area region of the spatially modeledroad network.

In the prior art, another critically important aspect requiring new andimproved understanding and enabling description relates to the modelingand processing of vehicular traffic data and information which areacquired, collected, or gathered, using techniques based on cellulartelephone types of mobile wireless communications networks or systems,which to date, feature relatively low accuracy and precision of vehiclelocations compared to the less widely used, but significantly morehighly accurate and precise, GPS types of mobile wireless communicationor electronic vehicular tracking technologies.

In prior art, another critically important aspect requiring new andimproved understanding and enabling description relates to the modelingand processing of vehicular traffic data and information which areacquired, collected, or gathered, from an ‘arbitrary’,non-pre-determined or non-designated, population or group of vehicleseach including a uniquely or specifically designated or assigned mobilesensor or electronic device, therefore, resulting in a potentially largenumber of mobile sensors or electronic devices providing dynamic vehiclelocation and velocity data and information to the computerized centraltraffic data and information handling system.

In the prior art, another critically important aspect requiring new andimproved understanding and enabling description relates to the properand efficient combining or fusing of a variety of vehicular traffic dataand information which are acquired, collected, or gathered, using acombination of a various techniques based on networks of fixed or statictraffic sensors or electronic devices, GPS and/or cellular telephonetypes of mobile wireless communications networks or systems, and,various other manual and electronic types of vehicular traffic data andinformation such as historical and/or event related vehicular trafficdata and information.

In the prior art, another important aspect requiring understanding andenabling description relates to techniques for protecting the privacy ofindividuals associated with or hosting the sources, that is, the mobilesensors or electronic devices, of vehicular traffic data and informationwhich are acquired, collected, or gathered, using techniques based onGPS and/or cellular telephone types of mobile wireless communicationsnetworks or systems. The inventors are unaware of any prior art teachingfor performing this in the field of vehicular traffic data andinformation.

There is thus a strong need for, and it would be highly advantageous tohave a method and system for modeling and processing vehicular trafficdata and information, and using the modeled and processed vehiculartraffic data and information for providing a variety of vehiculartraffic related service applications to end users. Moreover, there is aparticular need for such a generally applicable method and system withregard to using vehicular traffic data and information forcomprehensively, yet, accurately and practicably, describing current andpredicting future vehicular traffic situations and scenarios, from whichvehicular traffic data and information are used for providing thevariety of vehicular traffic related service applications to the endusers.

SUMMARY OF THE INVENTION

The present invention relates to a method and system for modeling andprocessing vehicular traffic data and information, and using the modeledand processed vehicular traffic data and information for providing avariety of vehicular traffic related service applications to end users.The present invention especially includes features for using vehiculartraffic data and information for comprehensively, yet, accurately andpracticably, describing current and predicting future vehicular trafficsituations and scenarios, from which vehicular traffic data andinformation are used for providing the variety of vehicular trafficrelated service applications to the end users.

Thus, according to the present invention, there is provided a method anda system for modeling and processing vehicular traffic data andinformation, comprising: (a) transforming a spatial representation of aroad network into a network of spatially interdependent and interrelatedoriented road sections, for forming an oriented road section network;(b) acquiring a variety of the vehicular traffic data and informationassociated with the oriented road section network, from a variety ofsources; (c) prioritizing, filtering, and controlling, the vehiculartraffic data and information acquired from each of the variety ofsources; (d) calculating a mean normalized travel time (NTT) value foreach oriented road section of said oriented road section network usingthe prioritized, filtered, and controlled, vehicular traffic data andinformation associated with each source, for forming a partial currentvehicular traffic situation picture associated with each source; (e)fusing the partial current traffic situation picture associated witheach source, for generating a single complete current vehicular trafficsituation picture associated with entire oriented road section network;(f) predicting a future complete vehicular traffic situation pictureassociated with the entire oriented road section network; and (g) usingthe current vehicular traffic situation picture and the future vehiculartraffic situation picture for providing a variety of vehicular trafficrelated service applications to end users.

The present invention successfully overcomes all the previouslydescribed shortcomings and limitations of presently known techniques foranalyzing, modeling, and processing, the acquired, collected, orgathered, vehicular traffic data and information. Especially with regardto using vehicular traffic data and information for comprehensively,yet, accurately and practicably, describing current and predictingfuture vehicular traffic situations and scenarios, from which vehiculartraffic data and information are used for providing a variety ofvehicular traffic related service applications to end users. Anotherimportant benefit of the present invention is that it is generallyapplicable and complementary to various different ‘upstream’ prior arttechniques of gathering, collecting, or acquiring, vehicular trafficdata and information, and, generally applicable and complementary tovarious different ‘downstream’ prior art techniques of electronicallycommunicating, sending, or distributing, the analyzed, modeled, andprocessed, vehicular traffic data and information in vehicular trafficrelated service applications to end users.

Implementation of the method and system for modeling and processingvehicular traffic data and information, and using the modeled andprocessed vehicular traffic data and information for providing a varietyof vehicular traffic related service applications to end users,according to the present invention, involves performing or completingselected tasks or steps manually, automatically, or a combinationthereof. Moreover, according to actual instrumentation and/or equipmentused for implementing a particular preferred embodiment of the disclosedinvention, several selected steps of the present invention could beperformed by hardware, by software on any operating system of anyfirmware, or a combination thereof. In particular, as hardware, selectedsteps of the invention could be performed by a computerized network, acomputer, a computer chip, an electronic circuit, hard-wired circuitry,or a combination thereof, involving any number of digital and/or analog,electrical and/or electronic, components, operations, and protocols.Additionally, or alternatively, as software, selected steps of theinvention could be performed by a data processor, such as a computingplatform, executing a plurality of computer program types of softwareinstructions or protocols using any suitable computer operating system.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention is herein described, by way of example only, withreference to the accompanying drawings, wherein:

FIG. 1 is a block flow diagram of a preferred embodiment of the methodand system for modeling and processing vehicular traffic data andinformation, and using the modeled and processed vehicular traffic dataand information for providing a variety of vehicular traffic relatedservice applications to end users, in accordance with the presentinvention;

FIG. 2 is a schematic diagram illustrating a preferred embodiment ofmodeling a road network in terms of ‘oriented road sections’ as part of,and in relation to, oriented road section network 14 of method/system 10of FIG. 1, in accordance with the present invention;

FIG. 3 is a pictorial diagram illustrating exemplary results obtainedfrom the process of path identification using vehicular traffic data andinformation acquired from mobile sensors of a cellular phone mobilecommunication network, in accordance with the present invention;

FIG. 4 is a pictorial diagram illustrating exemplary results obtainedfrom the process of path identification using vehicular traffic data andinformation acquired from mobile sensors of an anti-theft mobilecommunication network, in accordance with the present invention;

FIG. 5 is a schematic diagram illustrating a partial current vehiculartraffic situation picture featuring current NTT values obtained from anexemplary network source of mobile sensor vehicular traffic data andinformation, and indicated for each oriented road section of anexemplary part of an oriented road section network, in accordance withthe present invention;

FIG. 6 is a graph illustrating an example of vehicular traffic behaviorassociated with an exemplary oriented road section, in terms ofdifferent NTT values plotted as a function of time, in accordance withthe present invention;

FIG. 7 is a graphical diagram illustrating usage of a three-dimensionalvehicular traffic situation picture for providing route recommendationtypes of traffic related service applications to end users, inaccordance with the present invention; and

FIG. 8 is a graphical diagram illustrating usage of a three-dimensionalvehicular traffic situation picture for providing traffic alerts andalternative route recommendation types of traffic related serviceapplications to end users, in accordance with the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention takes priority from U.S. Provisional PatentApplication No. 60/227,905, filed Aug. 28, 2000, entitled “DynamicTraffic Flow Forecasting, Using Large Volumes Of Privacy ProtectedLocation Data”, the teachings of which are incorporated by reference asif fully set forth herein.

The present invention relates to a method and system for modeling andprocessing vehicular traffic data and information, and using the modeledand processed vehicular traffic data and information for providing avariety of vehicular traffic related service applications to end users.The present invention especially includes features for using vehiculartraffic data and information for comprehensively, yet, accurately andpracticably, describing current and predicting future vehicular trafficsituations and scenarios, from which vehicular traffic data andinformation are used for providing the variety of vehicular trafficrelated service applications to the end users.

The present invention features several aspects of novelty and inventivestep over the prior art, for developing better, more comprehensive,highly accurate and precise, yet, practicable and implementabletechniques for analyzing, modeling, and processing, the acquired,collected, or gathered, vehicular traffic data and information. Severalmain aspects of the present invention are briefly described herein.These and additional aspects of the present invention are described inmore detail thereafter.

One main aspect of the present invention relates to the adaptation andmodeling of a geographical information system (GIS), or, other similarlyorganized and detailed spatial representation of a network of roads, fora particular local or wide area region, within which the vehiculartraffic data and information are acquired, collected, or gathered. Inparticular, there is efficiently ‘spatially’ modeling the road network,in order to accommodate and fit the variety of vehicular traffic dataand information, while limiting the amount of data and informationneeded to be stored and handled. By way of this adaptation and modelingof a road network, especially a GIS type of road network, there is alsoproperly and efficiently ‘spatially’ modeling, interrelating, andcorrelating, the vehicular traffic data and information which areacquired, collected, or gathered, among a plurality of sub-regions,sub-areas, or, other designated sub-divisions, within the particularlocal or wide area region of the spatially defined road network.Furthermore, there is incorporating the factor or dimension of time, forproperly and efficiently ‘spatially and temporally’ defining,interrelating, and correlating, the vehicular traffic data andinformation which are acquired, collected, or gathered, among theplurality of sub-regions, sub-areas, or, other designated sub-divisions,within the particular local or wide area region of the spatially definedroad network.

Another main aspect of the present invention relates to the modeling andprocessing of vehicular traffic data and information which are acquired,collected, or gathered, using techniques based on mobile wirelesscommunications networks or systems, such as cellular telephone types ofnetworks and systems, which to date, feature relatively low accuracy andprecision of vehicle locations compared to the less widely used, butsignificantly more highly accurate and precise, GPS types of mobilewireless communication or electronic vehicular tracking technologies.

Another main aspect of the present invention relates to the modeling andprocessing of vehicular traffic data and information which are acquired,collected, or gathered, from an ‘arbitrary’, non-pre-determined ornon-designated, population or group of vehicles each including auniquely or specifically designated or assigned mobile sensor orelectronic device, therefore, resulting in a potentially large number ofmobile sensors or electronic devices providing dynamic vehicle locationand velocity data and information to the computerized central trafficdata and information handling system.

Another main aspect of the present invention relates to efficientcombining or fusing of a variety of vehicular traffic data andinformation which are acquired, collected, or gathered, using acombination of various techniques based on networks of fixed or statictraffic sensors or electronic devices, mobile wireless communicationsnetworks or systems such as GPS and/or cellular telephone networks orsystems, and, various other manual and electronic types of vehiculartraffic data and information such as traffic reports, incorporating inthe fusion process the historical and/or event related vehicular trafficdata and information that is accumulated, analyzed, and processed, frompreviously accumulated vehicular traffic data and information in thesame system.

Another main aspect of the present invention relates to techniques forprotecting the privacy of individuals associated with or hosting thesources, that is, the mobile sensors or electronic devices, of vehiculartraffic data and information which are acquired, collected, or gathered,using techniques based on GPS and/or cellular telephone types of mobilewireless communications networks or systems. The inventors are unawareof any prior art teaching for performing this in the field of vehiculartraffic data and information.

It is to be understood that the present invention is not limited in itsapplication to the details of the order or sequence of steps ofoperation or implementation of the method, or, to the details ofconstruction and arrangement of the various devices and components ofthe system, set forth in the following description and drawings. Forexample, the following description refers to a cellular telephone typeof mobile wireless communication network or system as the primary sourceof electronically acquired vehicular data and information, in order toillustrate implementation of the present invention. As indicated below,additionally, or, alternatively, other types of mobile wirelesscommunication networks or systems function as sources of electronicallyacquired vehicular data and information. Accordingly, the invention iscapable of other embodiments or of being practiced or carried out invarious ways. Also, it is to be understood that the phraseology andterminology employed herein are for the purpose of description andshould not be regarded as limiting.

Steps, components, operation, and implementation of a method and systemfor modeling and processing vehicular traffic data and information, andusing the modeled and processed vehicular traffic data and informationfor providing a variety of vehicular traffic related serviceapplications to end users, according to the present invention are betterunderstood with reference to the following description and accompanyingdrawings. Throughout the following description and accompanyingdrawings, like reference numbers refer to like elements.

Referring now to the drawings, FIG. 1 is a block flow diagram of apreferred embodiment of the method and system, hereinafter, generallyreferred to as method/system 10, of the present invention. Terminologyand referenced items appearing in the following description of FIG. 1are consistent with those used in FIGS. 1 through 8.

In Step (a) of the method of the present invention, there istransforming a spatial representation of a road network into a networkof spatially interdependent and interrelated oriented road sections, forforming an oriented road section network.

Specifically, there is transforming a spatial representation of a roadnetwork 12 into a network of spatially interdependent and interrelatedoriented road sections, for forming an oriented road section network 14.Preferably, road network 12 is a geographical information system (GIS)type of road network 12, which is well known in the art of vehicularroad traffic data and information. Typically, a GIS type of road network12 is a detailed spatial representation of a road network encompassing aparticular local or wide area region, within which are a plurality ofsub-regions, sub-areas, or, other designated sub-divisions, such asroads, turns, junctions, and, areas or regions of variably populatedstreets and roads. Associated with road network 12, and consequently,oriented road section network 14, are the vehicular traffic data andinformation which are acquired, collected, or gathered, among theplurality of sub-regions, sub-areas, or, other designated sub-divisions.Step (a) corresponds to an initialization step when method/system 10 isapplied to a new geographical region or road network 12. Vehiculartraffic data and information are subsequently added, by various manualand/or electronic means, to the oriented road section network 14 modelof a GIS type of road network 12.

The approach of the present invention to the field of vehicular trafficdata and information, in general, and to the problematic situation ofvehicular traffic congestion, in particular, is from a ‘holistic’ pointof view, emphasizing the importance of an entire road network 12encompassing a particular local or wide area region, and theinterdependence and interrelation of the plurality of sub-regions,sub-areas, or, other designated sub-divisions of road network 12. Thisis accomplished by adapting and transforming a spatial representation ofroad network 12 into a network of spatially interdependent andinterrelated oriented road sections, for forming oriented road sectionnetwork 14. This process is conceptually analogous to transforming roadnetwork 12 into an intertwined weave of oriented road sections, forforming oriented road section network 14.

FIG. 2 is a schematic diagram illustrating a preferred embodiment ofmodeling a road network in terms of ‘oriented road sections’ as part of,and in relation to, oriented road section network 14 of method/system 10of FIG. 1. In the upper part of FIG. 2, direction of vehicular trafficflow is indicated by the arrows drawn inside the road segments andinside the road junctions. Herein, the term ‘road section’, in general,represents a unit featuring at least one consecutive road segment of aroad network, preferably, a GIS type of road network, where the at leastone road segment, positioned head-to-tail relative to each other, arelocated between two road junctions, referred to as a tail end roadjunction and a head end road junction, within the road network, and arecharacterized by similar vehicular traffic data and information. Inpractice, typically, a ‘road section’ represents a unit featuring a setof a plurality of consecutive road segments of a road network,preferably, a GIS type of road network, where the consecutive roadsegments, positioned head-to-tail relative to each other, are locatedbetween two road junctions, a tail end road junction and a head end roadjunction, within the road network, and are characterized by similarvehicular traffic data and information. For purposes of effectivelymodeling a particular road network, a plurality of road segments aretherefore combined into a single homogeneous or uniform road sectioncharacterized by homogeneous or uniform vehicular traffic data andinformation. Accordingly, the road network is modeled as a plurality ofroad sections.

Accordingly, in the upper part of FIG. 2, generally indicated as 15, aroad section, as a first example, road section 16, represents a unitfeaturing a set of a plurality of, for example, two, consecutive roadsegments 18 and 20 of a road network (12 in FIG. 1, partly shown in FIG.2), where consecutive road segments 18 and 20 are positionedhead-to-tail relative to each other, are located between two roadjunctions, in particular, head end road junction 22 and tail end roadjunction 24, within the road network, and are characterized by similarvehicular traffic data and information. As a second example, roadsection 26, represents a unit featuring a set of a plurality of, forexample, three, consecutive road segments 28, 30, and, 32, within thesame road network (12 in FIG. 1, partly shown in FIG. 2), whereconsecutive road segments 28, 30, and, 32, are positioned head-to-tailrelative to each other, are located between two road junctions, inparticular, head end road junction 34 and tail end road junction 22,within the road network, and are characterized by similar vehiculartraffic data and information.

Two additional examples, relating to the two directions of abi-directional street, of a road section, are road section 36 and roadsection 38, each representing a single consecutive road segment 40,within the road network (12 in FIG. 1, partly shown in FIG. 2), whereconsecutive road segment 40 is positioned head-to-tail (relative toitself and oppositely for each of the two directions), located betweentwo road junctions, in particular, head end road junction 42 and tailend road junction 22, and, head end road junction 22 and tail end roadjunction 42, respectively, within the road network, and each ischaracterized by a particular vehicular traffic data and information,according to each of the two directions, respectively.

Herein, the term ‘oriented road section’ represents a road sectionhaving a single vehicular traffic continuation option located at thehead end road junction. Herein, a single vehicular traffic continuationoption refers to one of the various vehicular traffic flow options avehicle may take, such as optionally continuing to travel straight,optionally taking a right turn, or, optionally taking a left turn, froma particular road segment joined or linked to the head end roadjunction. Accordingly, a vehicular traffic continuation option isselected from the group consisting of continuing to travel straight,taking a right turn, and, taking a left turn, from a particular roadsegment joined or linked to the head end road junction.

Accordingly, in the lower part of FIG. 2, generally indicated as 50,shown are eight oriented road sections, that is, oriented road sections16 a, 16 b, 16 c, associated with road section 16; oriented roadsections 26 a and 26 b, associated with road section 26; oriented roadsection 36 a, associated with road section 36; and, oriented roadsections 38 a and 38 b, associated with road section 38. Each orientedroad section has a single vehicular traffic continuation option, thatis, a vehicular traffic continuation option selected from the groupconsisting of continuing to travel straight, taking a right turn, and,taking a left turn, from the particular road segment joined or linked tothe associated head end road junction, as shown by the arrows in thelower part of FIG. 2, corresponding to the arrows in the upper part ofFIG. 1, indicating direction of vehicular traffic flow drawn inside theroad segments and inside the road junctions.

An equally alternative way of defining the term oriented road section isthat, a road section having a head end road junction with a particularplurality of vehicular traffic continuation options is split or dividedinto that particular plurality of oriented road sections. Accordingly,in FIG. 2, for example, road section 16 having head end road junction 22with a plurality of three vehicular traffic continuation options, thatis, vehicular traffic continuation option 16 a (continuing to travelstraight), 16 b (taking a right turn), and, 16 c (taking a left turn),is split or divided into a plurality of three oriented road sections 16a, 16 b, and 16 c, respectively.

When special lanes in the various road segments within the oriented roadsection network are assigned to turning traffic, the respective orientedroad sections may yield different vehicular traffic data andinformation, such as different values relating to road congestion orheavy vehicular traffic volume. This representation of a road networkenables the incorporation of interdependence and interrelation among theplurality of road segments, the plurality of road sections, and, theplurality of oriented road sections. In particular, the oriented roadsection network model of a road network accounts for the significantinfluence of road junctions on vehicular traffic flow and associatedtravel time delays. Moreover, there is strong interdependence andinterrelation between any given particular oriented road sectioncharacterized by a particular traffic situation or scenario and otheroriented road sections in the same vicinity, either crossing or parallelto the particular oriented road section.

In Step (b), there is acquiring a variety of vehicular traffic data andinformation associated with the oriented road section network, from avariety of sources.

An important aspect of the present invention is the ability to model andprocess a wide variety of vehicular traffic data and information, inparticular, which are used for generating current and future vehiculartraffic situation pictures. Referring to method/system 10 of FIG. 1,sources of vehicular traffic data and information are selected from thegroup consisting of sources 60 of fixed sensors, sources 62 of mobilesensors, sources 64 of traffic reports by police or radio broadcasts ofvehicular traffic data and information, other sources 66, andcombinations thereof. Each of the variety of acquired, collected, or,gathered, vehicular traffic data and information, is characterized by avariable level of accuracy, and is independent of any other specificcharacteristics of the corresponding source.

Systems of fixed sensors and traffic reports of historical and/or eventrelated vehicular traffic data and information are presently the mostcommon sources of vehicular traffic data and information, and are wellknown in the art of vehicular traffic data and information. However, thepresent invention features mobile sensors as most advantageous foracquiring vehicular traffic data and information, even though they havelower confidence levels. A growing number of mobile wirelesscommunication devices are increasingly being installed or carried invehicles, and are capable of transmitting vehicular locations to acomputerized central data and information receiving and processingsystem. Such mobile wireless communication devices are telemetricdevices with GPS capabilities, anti-theft devices, and, driver-carrieddevices such as computer laptops and cellular phones. The respectivecomputerized central data and information receiving and processingsystems have capabilities of locating these devices, and act as sourcesof vehicular traffic data and information for assessing trafficsituations. The variability of these devices and the spreadingpopularity of some of them leads to an abundant and widespreadpopulation of mobile sensors. However, some of these systems, such as acellular telephone wireless communication networks, have poor locationaccuracy, and therefore require elaborate and unique processing in orderto extract useful vehicular traffic data and information.

In principle, method/system 10 of the present invention is applicable toall types of mobile sensor systems, and models and processes thevariability of location accuracy, vehicular traffic movement data andinformation, reading time intervals, and other types of vehiculartraffic data and information as further described below. Method/system10 acquires vehicular traffic data and information from several suchsources in parallel and combines or fuses the acquired vehicular trafficdata and information into one coherent and complete vehicular trafficsituation picture.

Acquiring the vehicular traffic data and information from a mobilesensor system is performed by tracking a sample of mobile sensors 62(FIG. 1) that are carried in moving vehicles. Locations of mobilesensors 62 are obtained from the mobile network in known time intervals,and accordingly, the path of the vehicle is identified in terms of aplurality of oriented road sections featured in the oriented roadsection network, for example, the plurality of exemplary oriented roadsections shown in FIG. 2 featured in oriented road section network 14(FIG. 1), and the velocity of a given vehicle on the different roadsections of the identified path are calculated. A statistical processingof the velocities of all mobile sensors 62 that traveled on a specificoriented road section during the time period of an assessment cycleyields a normalized travel time (NTT) value, hereinafter, also referredto as an NTT value, on that specific oriented road section, where thenormalized travel time refers to a travel time normalized with respectto a pre-determined distance, for example, in a non-limiting fashion,normalized with respect to a distance having a range of between about 10meters to about 100 meters, preferably, a distance of 100 meters. Whenmore than one such NTT value is calculated, a possibility of differentvelocities on different lanes of a particular oriented road section isindicated.

In Step (c), there is prioritizing, filtering, and controlling, thevehicular traffic data and information acquired from each of the varietyof sources.

In this step of implementing method/system 10, consideration is given tovarious aspects relating to prioritizing, filtering, and controlling,the vehicular traffic data and information acquired from each of thevariety of sources. For example, filtering noise corresponding toirrelevant sensors and erroneous data.

With respect to the choice of the sampled mobile sensor devices,sampling policy or prioritizing the variety of vehicular traffic dataand information, and controlling the sampling of the vehicular trafficdata and information, statistical consideration show that a very smallpercentage, approximately 1-2%, of moving vehicles provide a sufficientbase to obtain the necessary data. The number of moving sensors is,however, huge, and there is need to efficiently choose the samplepopulation, to decide on a sampling policy, and to control its carryingout, so that the data collected will be as relevant as possible for thepurpose of vehicular traffic assessment.

The choice of sample units is done by making sure that they are in highprobability moving-vehicle-carried. In cellular telephone networks, thisis done by identifying phones whose cell-handover rates indicate arelatively fast movement. A parallel procedure is performed for anymobile sensor source. In mobile anti-theft systems, for example,vehicular ignition activation is a valid indicator of‘movement-suspicion’. This initial choice of sensor population assures,for example, that pedestrians will not be included, and so prevents,along the track, the confusion between walking pedestrians and slowingtraffic due to heavy congestion.

Data acquisition from cellular phones is controlled by a serverconnected to a particular cellular phone network. The operation ofcellular phones provokes a large amount of administrative and controlmessages that flow in the cellular phone network whenever any cellular‘event’ occurs. For example, initiation of a call, completion of a call,transmission of a short message service (SMS) message, a cell transitionor handover, etc. The server of the cellular phone network monitorsthese messages and intercepts those that indicate ‘movement’ of cellularphone holders, for example, phones whose cell handovers rate can pointto a vehicle speed.

Those cellular phones that are suspected as being ‘vehicle-carried’ areimmediately tracked for their locations using the handovers themselvesas ‘footprints’ or ‘footsteps’, and/or other locating capability of thecellular phone network. Different particular cellular phone networks usedifferent locating techniques and systems, each having a differentcharacteristic locating accuracy. Method/system 10 is independent ofspecific locating techniques and systems, and is suited to a variabilityof accuracy and precision levels. For example, a common difficultyconcerning locating accuracy and precision of a mobile sensor network isthe phenomenon of ‘noise’, such as that caused by reflections, lowcell-efficiency management, and/or, even errors. As a result of this, asubstantial number of the footprints is erroneous, whereby they do notrepresent real or actual sensor locations.

The tracking of the ‘moving’ phones is done by polling their locationsin known time intervals. This operation is controlled by Samplersoftware module 1 indicated in FIG. 1. Sampler software module 1regulates the sampling so that it will be most efficient. Specifically,for example, Sampler software module 1 (i) filters out cellular phonesthat are recognized as ‘noise’, (ii) prioritizes the vehicular trafficdata and information acquisition according to policy or presentvehicular traffic circumstances, (iii) prevents tracking cellular phonesthat stopped moving, and, (iv) maintains the sampling procedure withinthe locating capacity of the particular cellular phone network.

Once the ‘moving’ sensors are identified, method/system 10 includes themin the sample population and controls the flow of the vehicular trafficdata and information according to actual features and capabilities ofmethod/system 10, in general, and of Sampler software module 1, inparticular, and, according to changing locations of the moving sensorsalong their respective paths. For example, Sampler software module 1obtains the vehicular traffic data and information by ‘push’ or ‘pull’procedures, that is, where ‘push’ is a mode of receiving the vehiculartraffic data and information that is initiated by source 62 of themobile sensors, and, where ‘pull’ is a mode of receiving the vehiculartraffic data and information when the initiator is Sampler softwaremodule 1. This tracking operation is controlled by method/system 10according to a predefined and updated policy. For instance, according toa policy of ‘not to track too many vehicles in a same region’, ‘focusthe tracking on a certain problematic region’, ‘stop tracking a vehiclethat stopped for a pre-determined time interval’, and, ‘collectvehicular traffic data and information within a certain limited capacityof the cellular phone network’.

As previously indicated, a main aspect of the present invention relatesto techniques for protecting the privacy of individuals associated with,or hosting, the sources 62 (FIG. 2) of the mobile sensors or electronicdevices, of the vehicular traffic data and information which areacquired, collected, or gathered, using techniques based on GPS and/orcellular telephone types of mobile wireless communications networks orsystems. Tracking vehicles without the knowledge and consent of theirdrivers may be considered as violation of privacy. Method/system 10, ingeneral, and technical architecture of method/system 10, in particular,are designed for deleting identities of the associated sample units, andfor deleting individual sensor tracks once their derived velocities areincorporated into calculations and processing associated with theplurality of oriented road sections within oriented road section network14.

Moreover, these processing steps take place in a server that isconnected to the mobile sensor source network. When more than one mobilesensor source provides vehicular traffic data and information tomethod/system 10, such a server is in communication with each of thedata providers. Tracked vehicles are not identified by using phonenumbers. Even within the short tracking times, mobile sensor identitiesare kept within the location of the cellular phone network, so thatduring the whole process, privacy of cellular phone users is protected.

In Step (d), there is calculating a mean normalized travel time (NTT)value for each oriented road section of the oriented road sectionnetwork using the prioritized, filtered, and controlled, vehiculartraffic data and information associated with each source, for forming apartial current vehicular traffic situation picture associated with eachsource.

Prioritized, filtered, and controlled, vehicular traffic data andinformation associated with each source are transmitted from Samplersoftware module 1 to NTT calculator software module 2, as indicated inFIG. 1. In this step of implementing method/system 10 (FIG. 1),consideration is given to assessment of normalized travel times (NTTs)values on oriented road sections, from individual calculated values ofNTTs, and, to inaccuracy problems relating to identifying the path of asample, and in extracting and calculating the NTT values.

Data from fixed sensors are usually received in terms of velocity or NTTvalues. Transition of these values for processing according to the modelof method/system 10 of the present invention is straightforward to oneof ordinary skill in the art, and is not further described herein.Vehicular traffic data and information obtained from traffic reports arehandled manually and used for determining specific NTT values onoriented road sections or the specific patterns to be used. Vehiculartraffic data and information from mobile sensor sources is processed ina more elaborate way. As previously indicated, above, in thisdescription of the preferred embodiments, source 62 of the plurality ofmobile sensors is represented as a cellular phone network, being themost complex one, but is generally applicable to other mobile wirelesscommunication networks or systems operating with a plurality of mobilesensors or electronic devices.

Specifically applicable to vehicular traffic data and informationassociated with the mobile cellular phone network source, there isidentifying a path taken by each vehicle and calculating a meannormalized travel time (NTT) value for each oriented road section oforiented road section network 14 (FIG. 1) using the prioritized,filtered, and controlled vehicular traffic data and information, forforming a partial current vehicular traffic situation picture associatedwith the cellular phone network source.

Location readings acquired from mobile sensor networks are the‘footprints’ of the sensor's track. When footprints are inaccurate, theyare represented by geometrical areas, whose form and size depend on theparticular features of the locating method. In cellular phone networks,footprints can be the cells themselves, with or without time-advancedata, and their graphic representation is by segments of circle sectorswhose coverage can reach dimensions between tens and thousands ofmeters. Another type of footprint associated with cellular phonenetworks is a cell-handover, whose graphic representation is dictated bythe relative position of the two cells involved in the ‘handover’.

Specific characteristics of the location data obtained from the varietyof mobile sensor networks varies with the network, whereby,method/system 10 accounts for them in a generic way, by implementingstatistical methods and algorithms that rely on some assumptions as tothe reasonable behavior of vehicular drivers. The path of the vehicle isdeduced out of several consecutive readings. The deduced path is thesequence of connected oriented road sections that cross the locationareas in the order of their appearance, and which join into a logicalroute to take between two points. In this step, some of the ‘noise’ isdiscovered, for example, footprints that went beyond bounds of a‘sensible’ travel route. Additionally, those mobile sensors that are notrelevant, for example, those carried in trains, are also identified.Those single footprints or irrelevant sample units are immediatelyrejected and their tracking is stopped.

The NTTs of the vehicle on every section of the deduced path iscalculated using the timings of the footprints. The vehicle's positionon the road at the reading's moment is determined by introducing someadditional assumptions, like minimal acceleration and minimal velocity.The size of the footprints of a specific source influences theresolution of the oriented road sections that can be valued. Actually,the method emphasizes the more congested roads, because theystatistically offer more readings. Those are identified even if they areminor roads, but, with worse accuracy. However, there will always besmaller streets and roads that this system will fail to exactlyidentify. Those areas of smaller streets, confined and closed withinhigher level arteries of the road network are referred to as ‘regions’in the road network. Method/system 10 identifies ‘passage’ of the mobilesensor in such a region, and determines the average NTT value in thatregion. Indetermination of specific streets within regions of the roadnetwork is insignificant for the purpose of implementing method/system10, because they usually behave in a similar way, otherwise specificstreets that are more congested would stand out in the first place.

NTT values on an individual oriented section are calculated using theresults obtained from all mobile sensors that passed that oriented roadsection, hence resulting in statistically determined NTT values for eachoriented road section. The unification of the individual NTT values intoa determined value per oriented road section is done with considerationof the confidence factor of each of the individual data. This confidencefactor is a function of the accuracy, the amount of footprints, theerror rate, and so on.

In the transition process from individual to comprehensive NTT values,an additional filtering stage takes place. Irregularities of somesensors may again indicate irrelevancy (stationed or slow movingvehicle, stop-and-go vehicles like trash collectors, etc.) or error.Data from those mobile sensors are rejected and filtered out of thecontinuation in the processing of the data. The outcome of this step inmethod/system 10 is an NTT picture of the oriented road sections for acertain time stamp, as calculated and assessed out of the specificnetwork of mobile sensors. Accordingly, there is forming a partialcurrent vehicular traffic situation picture associated with each networksource of mobile sensors.

As indicated in FIG. 1, prioritized, filtered, and controlled, vehiculartraffic data and information associated with each source are transmittedfrom Sampler software module 1 to NTT calculator software module 2. NTTcalculator software module 2 identifies the path taken by every vehiclefrom the consecutive footsteps of the mobile sensor associated with eachvehicle. A small number of low-accuracy locations can fit severalpossible paths, but with each additional footstep of the vehicle, thenumber of path alternatives is reduced, and the path is determined bymethod/system 10 only when the set of footsteps points to one path inhigh probability. The probability influences the confidentiality of thepath.

FIG. 3 is a pictorial diagram illustrating exemplary results obtainedfrom the process of path identification using vehicular traffic data andinformation acquired from mobile sensors of a cellular phone mobilecommunication network, and, FIG. 4 is a pictorial diagram illustratingexemplary results obtained from the process of path identification usingvehicular traffic data and information acquired from mobile sensors ofan anti-theft mobile communication network. For purposes of clarity,background 70 in FIG. 3, and, background 80 in FIG. 4, each correspondsto a GIS type representation of road network 12 (FIG. 1).

Sectors, for example, sectors 72, 74, and, 76, in FIG. 3, and, squares,for example, squares 82, 84, and, 86, in FIG. 4, represent thefootprints corresponding to the tracking data obtained from the twodifferent types of mobile communication networks, that is, from acellular phone mobile communication network, and, from an anti-theftmobile communication network, respectively. Location accuracy in FIGS. 3and 4 is approximately 0.5-1.0 km. In FIGS. 3 and 4, the path identifiedby NTT calculator software module 2 of method/system 10 is indicated bythe dark lined path, that is, dark lined path 90 in FIG. 3, and, darklined path 100 in FIG. 4, which is exactly the one taken by the vehicle,as shown by highly accurate readings, indicated by the set of smallsolid diamonds 92 in FIG. 3, and, by the set of small solid circles 102in FIG. 4, obtained from a GPS tracking system serving as control data.

Once a path is identified, the normalized travel times (NTTs) valuesbetween the footsteps are calculated, hence, NTT values of the pluralityof oriented road sections for an individual vehicle are assessed. Atthis stage, the determined path of the mobile sensor may be identifiedas crossing a ‘region’ of streets having low vehicular trafficcongestion. Ordinarily, footprint accuracy prevents identification ofspecific streets in such a region. NTT calculator software module 2processes such regions as a special type of oriented road section andallocates the region with a calculated NTT value that is a good averageindicating crossing time of the region, no matter which one of the innerroads is traveled along by the vehicle.

In the next part of Step (d) of method/system 10, individual NTT valuescalculated in a given processing cycle for every oriented road sectionare accumulated and statistically analyzed for providing a mean NTTvalue for each respective oriented road section. Eventually, more thanone NTT (statistical ‘peak’) value is identified, thereby, suggestingthe possibility of different lanes having different NTT values. Such asituation occurs, for example, when an oriented road section has severallanes, each with a different NTT value.

The overall data processing of Step (d) forms a current snapshot of NTTvalues on part of road network 12, and, thus, on part of orientedsection network 14 (FIG. 1). The partiality of the vehicular trafficsituation picture originates from the fact that only a portion of theroads in road network 12 are monitored in every calculation cycle. Thispartial picture is fused in the next step, Step (e), with similarpartial current vehicular traffic situation pictures obtained from othersources, if any, according to a particular application of method/system10, and, with specific reports received from traffic control authoritiesand others. FIG. 5 is a schematic diagram illustrating a partial currentvehicular traffic situation picture, generally indicated as 110,featuring current NTT values, obtained from an exemplary network source62 (FIG. 2) of mobile sensor vehicular traffic data and information, andindicated for each oriented road section of an exemplary part of anoriented road section network 14 (FIG. 1).

In Step (e), there is fusing the partial current traffic situationpicture associated with each source, for generating a single completecurrent vehicular traffic situation picture associated with the entireoriented road section network.

The partial current traffic situation picture associated with eachsource, for the plurality of sources of vehicular traffic data andinformation, is fused by a Fusion and current Picture generator module3, as indicated in FIG. 1, for generating a single complete currentvehicular traffic situation picture associated with the entire orientedroad section network. Due to the generic procedures of the dataprocessing of method/system 10 (FIG. 1), representations of the partialcurrent traffic situation pictures obtained from all mobile sources 62are similar. Vehicular traffic data and information acquired from othertypes of sources, for example, acquired from sources 60 of fixedsensors, sources 64 of traffic reports by police or radio broadcasts ofvehicular traffic data and information, and, sources 66, of other typesof vehicular traffic data and information, are respectively processed.The acquiring of vehicular traffic data and information from the varietyof sources, and integrating or fusing, by way of Fusion and currentPicture generator software module 3, into one coherent or completesingle current vehicular traffic situation picture is done in regulartime intervals. For example, in a non-limiting way, the sequence ofSteps (b)-(e) is performed at a pre-determined frequency in a range offrom about once per every two minutes to about once per every tenminutes.

‘Final’ NTT values for each of the oriented road sections is obtained byintegrating or fusing NTT values associated with all of the individualsources. NTT values associated with each source are weighed with aconfidence factor appropriate for each respective source, whereconfidence factors are determined by source parameters, such as locationaccuracy, and, quality and quantity of sensors per source. This currentvehicular traffic situation

In the first procedure, gaps are filled in using NTT values that arepredicted, for those missing oriented road sections, in previous cyclesof the process, that is, in previous time intervals of performing Steps(b)-(e) of method/system 10. In the initial cycles of the process, somedefault values are determined by the analysis of historical vehiculartraffic data and information, and road types. It is to be emphasized,that this stage of the overall process is continuously performed, andtherefore, each single complete current vehicular traffic situationpicture associated with the entire oriented road section network isbased on the combination of predictions and new vehicular traffic dataand information.

In the second procedure, gaps in the current partial vehicular trafficsituation picture are filled in by using a set of vehicular trafficrules for describing the interdependence, interrelation, and mutualcorrelation of vehicular traffic parameters among the plurality oforiented road sections in a particular region. Some of the vehiculartraffic rules supply default values of the vehicular traffic parameters.The vehicular traffic rules are derived by, and based on, analyzinghistorical vehicular traffic data and information.

In Step (f), there is predicting a future complete vehicular trafficsituation picture associated with the entire oriented road sectionnetwork.

Predicting a future complete vehicular traffic situation pictureassociated with the entire oriented road section network is performed bya Predictor software module 4, as indicated in FIG. 1. From the previousstep, Step (e), the single complete current vehicular traffic situationpicture associated with the entire oriented road section network, whichis obtained by fusing the partial current traffic situation pictureassociated with each source, for the plurality of sources of vehiculartraffic data and information, serves as a baseline or starting point forpredicting future layers of vehicular traffic situation pictures. Step(f) is performed at a pre-determined frequency in a range of from aboutonce per every two minutes to about once per every ten minutes.Typically, for implementation, in a non-limiting fashion, the frequencyof performing this step is usually less than the frequency of performingSteps (b)-(e).

The concept of the model of the comprehensive oriented road sectionnetwork 14 (FIG. 1), dictates the process and the set of predictiontools for implementing method/system 10. Forming current vehiculartraffic situation pictures, and forecasting or predicting futurevehicular traffic situation pictures, are determined with the aid ofvehicular traffic behavior patterns and rules, which are generated by aPatterns and rules generator module 6, as indicated in FIG. 1.

Vehicular traffic behavior patterns feature behavior rules of individualoriented road sections and correlation rules among the plurality ofdifferent oriented road sections, of the entire oriented road sectionnetwork 14. Accordingly, by using both types of rules, the step ofpredicting a future complete vehicular traffic situation pictureassociated with a particular oriented road section in a region withinthe entire oriented road section network, is influenced by both thecurrent vehicular traffic situation picture associated with thatparticular oriented road section, and, by the plurality of currentvehicular traffic situation pictures associated with other oriented roadsections in the same region of the entire oriented road section network.

FIG. 6 is a graph, general indicated as graph 120, illustrating anexample of vehicular traffic behavior associated with an exemplaryoriented road section, in terms of different NTT values plotted as afunction of time. Information featured in FIG. 6 demonstrates an exampleof an event identification and its influence on prediction of a futurevehicular traffic situation picture. The vehicular traffic behavior isassociated with an exemplary oriented road section out of historicalvehicular traffic data and information. When compared to calculated NTTvalues, indicated by hollow circles, determined from actual data, asignificant discrepancy is observed. In this example, calculated NTTvalues are significantly higher than pattern NTT values, indicated bysolid squares, determined from the vehicular traffic pattern based onhistorical vehicular traffic data and information. The resultingcorrection prediction NTT values, associated with the exemplary orientedroad section, are indicated by solid triangles. The correctionprediction NTT values are the NTT values that will be incorporated andfused into layers of future vehicular traffic situation pictures,associated with this particular oriented road section, for the‘prediction horizon’ time period. These layers are permanently correctedwith the formation of the current vehicular traffic situation picturesin following processing cycles.

The continuously updated comprehensive and complete current vehiculartraffic situation picture serves as a baseline of a three-dimensionalvehicular traffic forecast picture, where the horizontal planerepresents the roadmap and the vertical axis represents progression oftime. The three dimensional vehicular traffic situation picture isconstructed from discreet layers of vehicular traffic situationpictures, in time-intervals of a given processing cycle. The lower layervehicular traffic situation picture always corresponds to the currenttime, and higher layers of vehicular traffic situation picturescorrespond to future predictions.

Future time layers are produced by operating prediction tools that areproducts of the analysis of historical vehicular traffic data andinformation. This analysis is an on-going processing of the incomingvehicular traffic data and information, and the main resulting tools arebehavior patterns and correlation rules. A behavior pattern of aspecific oriented road section describes the regular changing of theassociated NTT values as a function of time, for example, during minuteand hourly progression of a day. Every oriented road section has its ownbehavior pattern, and different behavior patterns describe the behaviorin different circumstances, such as different days of the week,holidays, special events, weather conditions, and so on.

A correlation rule determines the correlation and interrelation of thevehicular traffic situation picture between different oriented roadsections as a function of time. Correlation rules are mostly if-thenrules. For example, they represent the fact that ‘if’ a road congestionis being observed on oriented road section A, ‘then’, a similar roadcongestion is expected to occur on oriented road section B after acertain period of time. These rules are the outcome of a data-miningoperation, also known as an advanced database searching procedure, onthe historical vehicular traffic data and information.

Oriented road sections tend to behave differently even without theattachment of a certain characteristic to each vehicular trafficbehavior pattern. The observance of values of the current vehiculartraffic situation picture in respect to optional vehicular trafficbehavior patterns, and the operation of vehicular traffic patternrecognition methods determines the vehicular traffic pattern thatdescribes the behavior of the oriented road section at an instant oftime.

Furthermore, while regular situations are handled with these tools,unexpected vehicular traffic developments are identified from thecurrent vehicular traffic situation pictures by comparing them toregular vehicular traffic behavior patterns. The set of several recentlycalculated NTT values on each oriented road section is compared to thepattern that describes the behavior of this oriented road section. Anysignificant discrepancy from the pattern activates a respectivecorrection of the NTT values predicted for this section in the nearfuture. The corrections are assuming continuous change rates and a‘back-to-normal’ return after a prediction horizon period, as previouslydescribed above and graphically illustrated in FIG. 6. If thediscrepancy identified is the result of a major event with prolongedinfluence, the acquired data in the subsequent processing cycles willprolong the correction period beyond the horizon period, until the gapbetween the regular pattern values and the acquired data is reduced.

Determination of the future behavior of individual oriented roadsections from their respective traffic behavior patterns is integratedwith the mutual horizontal influence of adjacent oriented road sectionsusing the previously described correlation rules. These rules canpredict, for example, the outcome of traffic events identified by thesaid discrepancies, and, predict their propagation in time alongadjacent, and non-adjacent, oriented road sections.

In Step (g), there is using the current vehicular traffic situationpicture and the future vehicular traffic situation picture for providinga variety of vehicular traffic related service applications to endusers.

Method/system 10 (FIG. 1) of the present invention especially includesfeatures for using the vehicular traffic data and information forcomprehensively, yet, accurately and practicably, describing current andpredicting future vehicular traffic situations and scenarios, from whichthe processed vehicular traffic data and information are used forproviding the variety of vehicular traffic related service applicationsto the end users. Using the current vehicular traffic situation pictureand the future vehicular traffic situation picture, obtained from theprevious processing steps of method/system 10 (FIG. 1), for providing avariety of vehicular traffic related service applications to end users,is performed by a Service engine software module 5, as indicated in FIG.1.

The unique structure of the three dimensional vehicular trafficsituation picture leads to a corresponding method of analyzing it, inorder to respond to traffic oriented queries associated with a varietyof traffic related service applications. This analysis is performed by adesignated software module, Service engine software module 5, for avariety of vehicular traffic related service applications 7, asindicated in FIG. 1. This software engine is capable of finding optimalroutes between given points, estimating travel times and initiatingalerts and route alterations when unexpected traffic events change thevehicular traffic situation picture. Any traffic related serviceapplication 7 featuring such queries can receive the necessary vehiculartraffic data and information by simply connecting to Service enginesoftware module 5 through the appropriate interface. FIG. 7 and FIG. 8show two exemplary ways in which three-dimensional vehicular trafficsituation pictures are used to enhance providing traffic related serviceapplications 7 to end users.

FIG. 7 is a graphical diagram illustrating usage of a three-dimensionalvehicular traffic situation picture for providing route recommendationtypes of traffic related service applications to end users. In FIG. 7,three-dimensional vehicular traffic situation picture, generallyindicated as 130, has each time-layer marked with its time-stamp(t-axis). In a current time, represented by lowest layer 132, a queryarrives from a service application 7 (FIG. 1), inquiring for the fastestroute from A to B. A recommendation based on the current vehiculartraffic situation picture alone is shown by the dotted line on righthand graph 134. With the three-dimensional prediction model, a parallelrecommendation analyzes the alternatives in a way that every particularoriented road section is chosen in respect to the predicted vehiculartraffic situation ‘at the time of passage of that particular orientedroad section’. The choice of the oriented road section is a functionboth of its predicted NTT value and the confidence factor of thatprediction. This last factor is determined for oriented road sectionsaccording to the regularity, the stability, and, the fluctuation rate ofthe historical behavior of each evaluated oriented road section.Comparative results of using a static analysis and the predictiveanalysis can be quite different, as shown in graph 134.

FIG. 8 is a graphical diagram illustrating usage of a three-dimensionalvehicular traffic situation picture for providing traffic alerts andalternative route recommendation types of traffic related serviceapplications to end users. In FIG. 8, three-dimensional vehiculartraffic situation picture, generally indicated as 140, has eachtime-layer marked with its time-stamp (t-axis). FIG. 8 shows the samemodel for initiation of alerts and recommendations of alternativeroutes. The ‘unexpected’ traffic event 142 that happens at the current,lower layer, time, is way off the recommended route. However, thecorrelation rules show that the influence of unexpected traffic event142 will propagate in time, resulting in road congestion that will reachone of the oriented road sections of the recommended route ‘at the timethat the vehicle is supposed to pass unexpected traffic event 142.Therefore, an alert is sent to the driver with a recommendation of oneor more known alternative routes.

Method/system 10 of the present invention provides vehicular trafficdata and information, and, provides a variety of vehicular trafficrelated service applications to end users, on the basis of existinginfrastructure of vehicular traffic data and information acquisition andcollection, such as by using a cellular phone mobile communicationnetwork, and the construction of centralized dynamic vehicular trafficsituation pictures, both current and future, for a geographical areawithin road network 12 (FIG. 1). The character of the vehicular trafficdata and information and processing of it requires unique, elaborate,and comprehensive, handling during the entire sequence of processingsteps, Steps (a) through (g), as is represented in the abovedescription.

The present invention is implemented for acquiring, modeling, andprocessing vehicular traffic data and information, in order to becompatible with a wide variety of end user types of traffic relatedservice applications. Therefore, it is a main aspect of the presentinvention that the ‘data providing mobile sensors’ are not limited tobeing those which are associated with the particular end users of suchtraffic related service applications. Hence, acquiring the vehiculartraffic data and information from the data providing network does notdepend at all on a specific group of mobile sensor device carrying endusers.

The final result of method/system 10 (FIG. 1) is, therefore, acontinuously on-going repetitive sequence of generating dynamicvehicular traffic situation pictures. The sequence is updated inpre-determined time intervals, as previously described above,preferably, in a range of between about once per every two minutes toabout once per every ten minutes. The described sequence of generatingdynamic three-dimensional vehicular traffic situation pictures serves asa highly accurate and precise data base for vehicular traffic relatedservice applications.

While the invention has been described in conjunction with specificembodiments and examples thereof, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart. Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims.

1. A method for modeling and processing vehicular traffic data, comprising the steps of: generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and acquiring vehicular traffic data associated with said road network from a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of said mobile sensors of said sample over the road network, the method further including a procedure for protecting privacy of individuals associated with said vehicle-carried mobile sensors by keeping the identities of relevant mobile sensors within said wireless telecommunications network.
 2. A method for modeling and processing vehicular traffic data, comprising the steps of: generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobiles sensors of said sample over the road network, the method further including a procedure for protecting privacy of individuals associated with said vehicle-carried mobile sensors by deleting the identities of said identified mobile sensors and/or by deleting said locations of mobile sensors of said tracked mobile sensors following processing.
 3. A method for modeling and processing vehicular traffic data, comprising the steps of: generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modeling and processing purposes, and wherein gaps in a current traffic situation for a road section due to a lack of current data are filled by using prediction based on historic vehicular traffic data.
 4. The method of claim 3, wherein gaps in the current traffic situation are filled by using vehicular traffic behavior patterns and/or correlation rules based on historical vehicular traffic data.
 5. The method of claim 4, wherein said behavior patterns of said road section network are time dependent.
 6. The method of claim 4, wherein said correlation rules of said road section network.
 7. The method of claim 4, wherein different behavior patterns are established for a road section at different times, days of the week, holidays and/or special events.
 8. The method of claim 5, wherein a said time dependent behavior pattern of a road section describes regular changing of associated normalized travel time (NTT) values as a function of time.
 9. The method of claim 5, wherein said time dependent correlation rule determines correlation and interrelation of each said single complete current vehicular traffic situation picture between different road sections as a function of time.
 10. The method of claim 3, further comprising the step of: predicting at least one future time vehicular traffic situation associated with at least a portion of said road section network.
 11. The method of claim 10, wherein the current vehicular traffic situation serves as a baseline or starting point for predicting said at least one future time vehicular traffic situation.
 12. The method of claim 10, wherein said predicting is performed at a pre-determined frequency in a range of from about once per every two minutes to about once per every ten minutes.
 13. The method of claim 10, wherein said predicting includes using said time dependent behavior patterns of said road section network and time dependent correlation rules of said road section network.
 14. The method of claim 10, wherein said predicting includes identifying unexpected vehicular traffic developments from a said current vehicular traffic situation by comparing said developments to regular time dependent behavior patterns of said road section network.
 15. The method of claim 14, wherein said predicting includes predicting propagation of the affects of traffic developments identified in time along adjacent and non-adjacent road sections, using time dependent correlation rules of said road section network.
 16. The method of claim 3, further comprising the step of providing a vehicular traffic related service application to an end user.
 17. A method for modeling and processing vehicular traffic data, comprising the steps of: generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and acquiring vehicular traffic data associated with said road network from at least a plurality of sensors and predicting a predicting a future traffic situation, wherein acquiring vehicular traffic data comprises tracking locations of a sample of said sensors for modeling a current vehicular situation, and wherein predicting a traffic situation comprises predicting normalized travel times over a plurality of road sections based on time dependent vehicle behavior patterns, such that the predicted traffic parameter for a particular road section takes into account the time of passage of that road section.
 18. A method for modeling and processing vehicular traffic data, comprising the steps of: generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network and predicting a future traffic situation, wherein acquiring vehicular traffic data comprises tracking locations of a sample of said mobile sensors for modeling a current vehicular situation, and wherein predicting comprises predicting travel times over a plurality of road sections based on time dependent vehicle behavior patterns, the method being further operable to compare detected traffic developments to regular said time dependent behavior patterns of said road section network, and to identify discrepancies from said regular time dependent behavior patterns, whereby propagation in time along adjacent and non-adjacent said road sections of traffic events identified by discrepancies from said time dependent behavior patterns can be determined using time dependent correlation rules of said road section network.
 19. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data and information, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a cellular wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising a mechanism for protecting the privacy of individuals associated with said vehicle-carried mobile sensors by keeping the identities of relevant mobile sensors within said wireless cellular telecommunications network.
 20. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data and information, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a cellular wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising a mechanism for protecting the privacy of individuals associated with said vehicle-carried mobile sensors by deleting the identities of relevant mobile sensors and/or by deleting said locations of said tracked mobile sensors following processing.
 21. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data and information, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising a module for filling in gaps in a current vehicular traffic data by predictions based on time dependent vehicular patterns derived from historic data, such that a predicted traffic parameter for a particular road section takes into account the time of passage of that road section.
 22. The system of claim 21, the gaps in the current traffic situation are filled by using time dependent correlation rules.
 23. The system of claim 21, wherein different behavior patterns are established for a road section at days of the week, holidays and/or special events.
 24. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising software for filling in gaps in current vehicular traffic data by predictions based on time dependent vehicular patterns derived from historic data, wherein the system is further operable to compare detected developments to regular said time dependent behavior patterns of said road section network and to identify discrepancies from said regular time dependent behavior patterns.
 25. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising a module to compare detected developments to regular said time dependent behavior patterns of said road section network and to identify discrepancies from said regular time dependent behavior patterns, whereby the system further determines propagation in time of traffic events identified by discrepancies from said time dependent behavior patterns of said road section network, using time dependent correlation rules and time dependent behavior patterns of said road section network.
 26. A system for modeling and processing vehicular traffic data, comprising: a mechanism for representing a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections; and software for acquiring vehicular traffic data associated with said road network from a sample of a plurality of mobile sensors in communication with a wireless telecommunications network, the software comprising instructions for selecting said sample and for tracking locations of mobile sensors of said sample over the road network, the system further comprising a module operable to compare detected developments to regular said time dependent behavior patterns of said road section network and to identify discrepancies from said regular time dependent behavior patterns, whereby the system further determines propagation in time along adjacent and non-adjacent said road sections, of traffic events identified by discrepancies from said time dependent behavior patterns of said road section network, using time dependent correlation rules of said road section network. 